Abstract
Event-Event Relation Detection (RD\(_{2e}\)) aims to detect the relations between a pair of news events, such as Causal relation between Criminal and Penal events. In general, RD\(_{2e}\) is a challenging task due to the lack of explicit linguistic feature signaling the relations. We propose a cross-scenario inference method for RD\(_{2e}\). By utilizing conceptualized scenario expression and graph-based semantic distance perception, we retrieve semantically similar historical events from Gigaword. Based on explicit relations of historical events, we infer implicit relations of target events by means of transfer learning. Experiments on 10 relation types show that our method outperforms the supervised models.
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This work was supported by the national Natural Science Foundation of China via Nos. 2017YFB1002104, 61672368 and 61672367.
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Hong, Y., Zhang, J., Song, R., Yao, J. (2018). Cross-Scenario Inference Based Event-Event Relation Detection. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_22
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DOI: https://doi.org/10.1007/978-3-319-99501-4_22
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